ArticleJournal of cheminformatics2024
Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Phytochemical Characterization ofFood science & nutrition · 2026Article
- Development and application of artificial intelligence in traditional Chinese medicine research and development.Chinese medicine · 2026Review
- Chemical Composition and Biological Activities ofMolecules (Basel, Switzerland) · 2025Review
- Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- AI driven network pharmacology: Multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis.Computational and structural biotechnology journal · 2025Review
- Discovering molecules and plants with potential activity against gastric cancer: anFrontiers in bioinformatics · 2025Article
- Targeting immune cell migration as therapy for inflammatory disease: a review.Frontiers in immunology · 2025Review
- CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis.Journal of cheminformatics · 2024Article
- Selected honey as a multifaceted antimicrobial agent: review of compounds, mechanisms, and research challenges.Future microbiologyReview
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top
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